Perform Multivariate-Cluster Analysis - Statistics for Each Variable - Statistics Assignment Help

Assignment Task :

The Data
For assignment 3 we will be using a selection of data from the Ontario census, reported at the dissemination area (DA). These data are stored as a shapefile with several columns describing a variety of socio-economic variables for Ontario Census DAs from the 2016 census.
The variables are described in more detail in the metadata alongside the shapefile.

Your Assignment
Your assignment is to perform multivariate-cluster analysis on the Ontario census data to generate a regionalization of Ontario’s DA for the purpose of socio-economic profiling. You can frame your assignment in any way you choose (e.g., business profiling, health, marketing, etc.).

Tasks
1. Choose a single economic area (EA) to focus on, subset the data appropriately.
2. Select a subset of 6 variables from the dataset to focus on for your analysis.
• At least one should be derived by combining two or more variables in some meaningful way.
3. Report initial descriptive statistics for each variable.
4. Show two (2) plots that are meaningful in the context of your problem.
5. Show two (2) choropleth maps that are meaningful in the context of your problem.
6. Scale your variables appropriately so that you remove the effect of different units/scales.
7. Multivariate clustering
• using your chosen variables run multivariate cluster analysis (k-means) for values of k = 2,3,. . . ,10.
• Record the cluster statistics including the Between/Total SS ratio for each value of k.
• Calculate the Davies-Bouldin index for each value of k.
• Choose the “best” clustering level.
8. Report and comment on the summary statistics for the cluster centres for your chosen value of k.
• cluster sizes
• cluster centers
• cluster within SS
9. Create meaningful names for each cluster in your final clustering based on the above summaries.
10. Create a bivariate scatterplot showing two of your 6 variables (you choose) with each point colored by cluster name.
11. Create a final cluster map that shows the distribution of your clusters across Southern ontario (within your EA)
• use the sf plotting functions
• use appropriate colors, titles, legend, etc.
12. Comment/interpret your findings in terms of how you framed your original problem.
 

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